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Shared memories of event details in the human brain are altered by misinformation and test expectations.

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The authors' code

Python · 340 lines · 11 KB · CC-BY-4.0

  1. #!/usr/bin/env python3
  2. """Calculate ROI-based inter-subject pattern similarity (ISPS) for one subject pair.
  4. Subject-pair, single-ROI mode. Four input tables are required: subject A
  5. behavior + neural pattern, subject B behavior + neural pattern. Computes
  6. ISPS for one ROI between the two subjects.
  8. WSLD: similarity to the partner's vector for the same stimulus.
  9. BSLD: mean similarity to the partner's *other slides within
  10. the same event*.
  11. """
  13. from __future__ import annotations
  15. import argparse
  16. import csv
  17. import math
  18. import os
  19. from collections import defaultdict
  20. from typing import Dict, Iterable, List, Sequence, Tuple
  23. PAIR_OUTPUT_COLUMNS = [
  24. "paired_id",
  25. "subject_id",
  26. "pair_subject_id",
  27. "stim_events",
  28. "stim_slides",
  29. "stim_type",
  30. "memory_type",
  31. "smltp",
  32. "stim_version",
  33. "isps",
  34. ]
  36. PAIR_BEHAVIOR_COLUMNS = {
  37. "subject_id",
  38. "stim_events",
  39. "stim_slides",
  40. "stim_type",
  41. "stim_version",
  42. }
  43. PAIR_NEURAL_ID_COLUMNS = {"subject_id", "stim_events", "stim_slides"}
  46. def parse_args() -> argparse.Namespace:
  47. parser = argparse.ArgumentParser(
  48. description="Compute single-ROI inter-subject pattern similarity for a subject pair."
  49. )
  50. parser.add_argument("--output", help="Output CSV/TSV path.")
  51. parser.add_argument("--output-dir", help="Output directory for pair/ROI mode.")
  52. parser.add_argument("--subject-a-behavior", help="Subject A behavior TSV/CSV.")
  53. parser.add_argument("--subject-a-neural", help="Subject A single-ROI neural TSV/CSV.")
  54. parser.add_argument("--subject-b-behavior", help="Subject B behavior TSV/CSV.")
  55. parser.add_argument("--subject-b-neural", help="Subject B single-ROI neural TSV/CSV.")
  56. parser.add_argument("--roi-name", help="ROI name used in the pair/ROI output filename.")
  57. return parser.parse_args()
  60. def delimiter_for_path(path: str) -> str:
  61. return "\t" if path.endswith((".tsv", ".tsv.gz")) else ","
  64. def read_table(path: str) -> List[Dict[str, str]]:
  65. delimiter = delimiter_for_path(path)
  66. with open(path, newline="") as f:
  67. rows = list(csv.DictReader(f, delimiter=delimiter))
  68. if not rows:
  69. raise ValueError(f"Input table has no rows: {path}")
  70. return rows
  73. def require_columns(rows: List[Dict[str, str]], required: set[str], label: str) -> None:
  74. missing = required - set(rows[0])
  75. if missing:
  76. raise ValueError(f"{label} is missing required columns: {sorted(missing)}")
  79. def clean_str(value: object) -> str:
  80. return str(value).strip()
  83. def pearson(x: Sequence[float], y: Sequence[float]) -> float:
  84. pairs = [
  85. (float(a), float(b))
  86. for a, b in zip(x, y)
  87. if not (math.isnan(float(a)) or math.isnan(float(b)))
  88. ]
  89. if len(pairs) < 2:
  90. return math.nan
  92. xs = [a for a, _ in pairs]
  93. ys = [b for _, b in pairs]
  94. mean_x = sum(xs) / len(xs)
  95. mean_y = sum(ys) / len(ys)
  96. dx = [a - mean_x for a in xs]
  97. dy = [b - mean_y for b in ys]
  98. ss_x = sum(a * a for a in dx)
  99. ss_y = sum(b * b for b in dy)
  100. if ss_x <= 0 or ss_y <= 0:
  101. return math.nan
  102. return sum(a * b for a, b in zip(dx, dy)) / math.sqrt(ss_x * ss_y)
  105. def fisher_z(r: float) -> float:
  106. """Return Fisher's z transform for a Pearson correlation."""
  107. if r is None or math.isnan(float(r)):
  108. return math.nan
  109. clipped = max(min(float(r), 0.999999), -0.999999)
  110. return math.atanh(clipped)
  113. def average(values: Iterable[float]) -> float:
  114. vals = [v for v in values if not math.isnan(v)]
  115. if not vals:
  116. return math.nan
  117. return sum(vals) / len(vals)
  120. def subject_pair_label(subject_a: str, subject_b: str) -> str:
  121. return "_".join(sorted([clean_str(subject_a), clean_str(subject_b)]))
  124. def pair_version_label(version_a: str, version_b: str) -> str:
  125. return "same" if clean_str(version_a) == clean_str(version_b) else "diff"
  128. def sort_key_event_slide(row: Dict[str, object]) -> Tuple[int, int, str, str]:
  129. try:
  130. event = int(clean_str(row["stim_events"]))
  131. except ValueError:
  132. event = 0
  133. try:
  134. slide = int(clean_str(row["stim_slides"]))
  135. except ValueError:
  136. slide = 0
  137. return event, slide, clean_str(row["subject_id"]), clean_str(row["stim_slides"])
  140. def merge_subject_behavior_neural(
  141. behavior_path: str,
  142. neural_path: str,
  143. ) -> List[Dict[str, object]]:
  144. behavior_rows = read_table(behavior_path)
  145. neural_rows = read_table(neural_path)
  146. require_columns(behavior_rows, PAIR_BEHAVIOR_COLUMNS, behavior_path)
  147. require_columns(neural_rows, PAIR_NEURAL_ID_COLUMNS, neural_path)
  149. voxel_cols = [col for col in neural_rows[0] if col not in PAIR_NEURAL_ID_COLUMNS]
  150. if not voxel_cols:
  151. raise ValueError(f"{neural_path} must include one or more voxel columns.")
  153. neural_by_key: Dict[Tuple[str, str, str], Dict[str, str]] = {}
  154. for row in neural_rows:
  155. key = (
  156. clean_str(row["subject_id"]),
  157. clean_str(row["stim_events"]),
  158. clean_str(row["stim_slides"]),
  159. )
  160. neural_by_key[key] = row
  162. merged: List[Dict[str, object]] = []
  163. event_versions: Dict[Tuple[str, str], str] = {}
  164. for row in behavior_rows:
  165. key = (
  166. clean_str(row["subject_id"]),
  167. clean_str(row["stim_events"]),
  168. clean_str(row["stim_slides"]),
  169. )
  170. if key not in neural_by_key:
  171. raise ValueError(
  172. "No neural row for behavior key "
  173. f"subject={key[0]} event={key[1]} slide={key[2]}"
  174. )
  176. event_key = (key[0], key[1])
  177. version = clean_str(row["stim_version"])
  178. previous_version = event_versions.setdefault(event_key, version)
  179. if previous_version != version:
  180. raise ValueError(
  181. "stim_version must be constant within each subject/event: "
  182. f"subject={key[0]} event={key[1]}"
  183. )
  185. neural_row = neural_by_key[key]
  186. merged.append(
  187. {
  188. "subject_id": key[0],
  189. "stim_events": key[1],
  190. "stim_slides": key[2],
  191. "stim_type": clean_str(row["stim_type"]),
  192. "memory_type": clean_str(row.get("memory_type", "")),
  193. "stim_version": version,
  194. "vector": [float(neural_row[col]) for col in voxel_cols],
  195. }
  196. )
  198. return sorted(merged, key=sort_key_event_slide)
  201. def compute_pair_roi_isps_rows(
  202. subject_a_rows: List[Dict[str, object]],
  203. subject_b_rows: List[Dict[str, object]],
  204. ) -> List[Dict[str, object]]:
  205. if not subject_a_rows or not subject_b_rows:
  206. return []
  208. subject_a = clean_str(subject_a_rows[0]["subject_id"])
  209. subject_b = clean_str(subject_b_rows[0]["subject_id"])
  210. subject_pair = subject_pair_label(subject_a, subject_b)
  212. rows_by_subject = {
  213. subject_a: subject_a_rows,
  214. subject_b: subject_b_rows,
  215. }
  216. rows_by_subject_event_slide: Dict[Tuple[str, str, str], Dict[str, object]] = {}
  217. rows_by_subject_event: Dict[Tuple[str, str], List[Dict[str, object]]] = defaultdict(list)
  219. for subject_rows in rows_by_subject.values():
  220. for row in subject_rows:
  221. key = (
  222. clean_str(row["subject_id"]),
  223. clean_str(row["stim_events"]),
  224. clean_str(row["stim_slides"]),
  225. )
  226. rows_by_subject_event_slide[key] = row
  227. rows_by_subject_event[(key[0], key[1])].append(row)
  229. output_rows: List[Dict[str, object]] = []
  230. for subject_id, pair_subject_id in ((subject_a, subject_b), (subject_b, subject_a)):
  231. for target in rows_by_subject[subject_id]:
  232. event = clean_str(target["stim_events"])
  233. slide = clean_str(target["stim_slides"])
  234. pair_same_key = (pair_subject_id, event, slide)
  235. if pair_same_key not in rows_by_subject_event_slide:
  236. continue
  238. pair_same = rows_by_subject_event_slide[pair_same_key]
  239. pair_event_rows = [
  240. row
  241. for row in rows_by_subject_event[(pair_subject_id, event)]
  242. if clean_str(row["stim_slides"]) != slide
  243. ]
  244. if not pair_event_rows:
  245. continue
  247. wsld = fisher_z(pearson(target["vector"], pair_same["vector"]))
  248. bsld = average(
  249. fisher_z(pearson(target["vector"], pair_row["vector"]))
  250. for pair_row in pair_event_rows
  251. )
  252. stim_version = pair_version_label(target["stim_version"], pair_same["stim_version"])
  254. common = {
  255. "paired_id": subject_pair,
  256. "subject_id": subject_id,
  257. "pair_subject_id": pair_subject_id,
  258. "stim_events": event,
  259. "stim_slides": slide,
  260. "stim_type": clean_str(target["stim_type"]),
  261. "memory_type": clean_str(target.get("memory_type", "")),
  262. "stim_version": stim_version,
  263. }
  264. output_rows.append({**common, "smltp": "wsld", "isps": wsld})
  265. output_rows.append({**common, "smltp": "bsld", "isps": bsld})
  267. return sorted(
  268. output_rows,
  269. key=lambda row: (
  270. int(row["stim_events"]),
  271. int(row["stim_slides"]),
  272. row["subject_id"],
  273. row["smltp"],
  274. ),
  275. )
  278. def write_pair_rows(path: str, rows: Iterable[Dict[str, object]]) -> None:
  279. output_dir = os.path.dirname(path)
  280. if output_dir:
  281. os.makedirs(output_dir, exist_ok=True)
  283. delimiter = delimiter_for_path(path)
  284. with open(path, "w", newline="") as f:
  285. writer = csv.DictWriter(f, fieldnames=PAIR_OUTPUT_COLUMNS, delimiter=delimiter)
  286. writer.writeheader()
  287. for row in rows:
  288. clean_row = dict(row)
  289. isps = clean_row["isps"]
  290. clean_row["isps"] = (
  291. "" if isps is None or math.isnan(float(isps)) else f"{float(isps):.8f}"
  292. )
  293. writer.writerow(clean_row)
  296. def run_pair_roi_mode(args: argparse.Namespace) -> str:
  297. required_args = {
  298. "--subject-a-behavior": args.subject_a_behavior,
  299. "--subject-a-neural": args.subject_a_neural,
  300. "--subject-b-behavior": args.subject_b_behavior,
  301. "--subject-b-neural": args.subject_b_neural,
  302. "--roi-name": args.roi_name,
  303. }
  304. missing_args = [name for name, value in required_args.items() if not value]
  305. if missing_args:
  306. raise SystemExit("Pair/ROI mode is missing: " + ", ".join(missing_args))
  308. subject_a_rows = merge_subject_behavior_neural(
  309. args.subject_a_behavior,
  310. args.subject_a_neural,
  311. )
  312. subject_b_rows = merge_subject_behavior_neural(
  313. args.subject_b_behavior,
  314. args.subject_b_neural,
  315. )
  316. rows = compute_pair_roi_isps_rows(subject_a_rows, subject_b_rows)
  318. subject_pair = subject_pair_label(
  319. subject_a_rows[0]["subject_id"],
  320. subject_b_rows[0]["subject_id"],
  321. )
  322. output_path = args.output
  323. if not output_path:
  324. output_dir = args.output_dir or "."
  325. output_path = os.path.join(
  326. output_dir,
  327. f"{subject_pair}_{args.roi_name}_isps.tsv",
  328. )
  330. write_pair_rows(output_path, rows)
  331. return output_path
  334. def main() -> None:
  335. args = parse_args()
  336. run_pair_roi_mode(args)
  339. if __name__ == "__main__":
  340. main()

01_calculate_roi_isps.py, under CC-BY-4.0 · at the source

Overview

Authors: Xuhao Shao1,2,3, Chuansheng Chen4, Elizabeth F. Loftus4, Bi Zhu1,2,3
ORCID iDs: Xuhao Shao, Chuansheng Chen, Bi Zhu
  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
  2. Institute of Developmental Psychology, Beijing Normal University, Beijing, China
  3. IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
  4. Department of Psychology, University of California, Irvine, California, United States of America
Institutions: Beijing Normal University (China); State Key Laboratory of Cognitive Neuroscience and Learning; University of California, Irvine (United States)
Journal: PLoS biology, volume 24, issue 7, article e3003886
Dates: received 30 December 2025; accepted 17 June 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003886 · PMID 42406796 · PMCID PMC13336189 · OpenAlex W7167508305
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging
MeSH: Brain*, Communication*, Memory*, Mental Recall*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Journal subjects: Biology and Life Sciences, Neuroscience, Cognitive Science, Cognition, Memory, Memory Recall, Learning and Memory, False Memories, Anatomy, Brain, Prefrontal Cortex, Medicine and Health Sciences, Cognitive Psychology, Perception, Sensory Perception, Vision, Psychology, Social Sciences, Brain Mapping, Functional Magnetic Resonance Imaging, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Neuroimaging, Physical Sciences, Mathematics, Discrete Mathematics, Combinatorics, Permutation, Hippocampus
Topic: Memory Processes and Influences (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2021ZD0200500); National Natural Science Foundation of China (32330039, 31971000); Fundamental Research Funds for the Central Universities (2243300006); China Postdoctoral Science Foundation (2024M750210)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Shared memories of event details are crucial to eyewitness testimony. When different people encode or recall the same event, similar scene-specific neural activity patterns emerge across individual brains. However, it remains unclear whether these patterns are specific to event details and how test expectancy (i.e., expecting free recall or general memory tests) and misinformation affect them. In this study, 100 participants were randomly assigned to view one of two versions of each event. Both versions featured identical scenarios, but with different details. About half of the participants were informed about the upcoming free recall before viewing events, while the others were told to expect a general memory test. Functional magnetic resonance imaging was used to record their brain activity during four stages: viewing original events, initial free recall, reading misinformation, and final free recall of original events. The neuroimaging data were analyzed based on the similarity of neural patterns across participants. Test expectancy increased the similarity of detail-specific neural activity patterns between individuals when they viewed original events in brain regions relevant for visual attention. Misinformation increased the likelihood of people forming shared false memories of event details. People who formed shared false memories exhibited similar detail-specific patterns of activity in the dorsomedial prefrontal cortex when reading misinformation. People who formed shared true memories exhibited similar detail-specific patterns of activity in the inferior parietal lobe when viewing original events, as well as in the ventrolateral prefrontal cortex and middle temporal gyrus when recalling them after exposure to misinformation. Our findings revealed that different brain regions of the default mode network play distinct roles in the encoding and recall of event details shared by individuals.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Its files are read in the Code ↔ Paper reader above.

Zenodo 20656852

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2), Python (1)
Size: 3 files, 3 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files), lme4 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: doi.org/10.5281/zenodo.20656852

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
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  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability

All relevant data is available within the manuscript and Supporting information files. Analysis code is available on Zenodo at: https://doi.org/10.5281/zenodo.20656852.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 11 MeSH terms, 4 funders, 82 references.

Cite

This paper

Shao, X., Chen, C., Loftus, E. F., & Zhu, B. (2026). Shared memories of event details in the human brain are altered by misinformation and test expectations. PLoS biology, 24(7), e3003886. https://doi.org/10.1371/journal.pbio.3003886

BibTeX

@article{shao2026shared,
author = {Shao, Xuhao and Chen, Chuansheng and Loftus, Elizabeth F. and Zhu, Bi},
title = {{Shared memories of event details in the human brain are altered by misinformation and test expectations}},
journal = {PLoS biology},
year = {2026},
month = jul,
volume = {24},
number = {7},
pages = {e3003886},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003886},
url = {https://doi.org/10.1371/journal.pbio.3003886},
pmid = {42406796},
pmcid = {PMC13336189}
}

RIS

TY - JOUR
AU - Shao, Xuhao
AU - Chen, Chuansheng
AU - Loftus, Elizabeth F.
AU - Zhu, Bi
TI - Shared memories of event details in the human brain are altered by misinformation and test expectations
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/07/06
VL - 24
IS - 7
SP - e3003886
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003886
UR - https://doi.org/10.1371/journal.pbio.3003886
LA - en
ER -

CSL-JSON

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"title": "Shared memories of event details in the human brain are altered by misinformation and test expectations",
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"family": "Shao",
"given": "Xuhao"
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"given": "Elizabeth F."
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"given": "Bi"
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"container-title-short": "PLoS Biol",
"volume": "24",
"issue": "7",
"page": "e3003886",
"DOI": "10.1371/journal.pbio.3003886",
"PMID": "42406796",
"PMCID": "PMC13336189",
"ISSN": "1544-9173",
"publisher": "PLOS",
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"language": "en",
"issued": {
"date-parts": [
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